{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no surfacing</th>\n",
       "      <th>flippers</th>\n",
       "      <th>fish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no surfacing  flippers fish\n",
       "0             1         1  yes\n",
       "1             1         1  yes\n",
       "2             1         0   no\n",
       "3             0         1   no\n",
       "4             0         1   no"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {'no surfacing':[1,1,1,0,0],\n",
    "                'flippers':[1,1,0,1,1],\n",
    "                'fish':['yes','yes','no','no','no']}\n",
    "df = pd.DataFrame(raw_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calEnt(df):\n",
    "    p = df.iloc[:,-1].value_counts(True)\n",
    "    l = -np.log2(p)\n",
    "    return np.sum(l * p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calEnt(df):\n",
    "    p = df.iloc[:,-1].value_counts(True)\n",
    "    return np.sum(-p*np.log2(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no surfacing</th>\n",
       "      <th>flippers</th>\n",
       "      <th>fish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no surfacing  flippers fish\n",
       "0             1         1  yes\n",
       "1             1         1  yes\n",
       "2             1         0   no\n",
       "3             0         1   no\n",
       "4             0         1   no"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bestSplit(df):\n",
    "    bestFeature = ''\n",
    " \n",
    "    E0 = calEnt(df)\n",
    "    fts_n = df.shape[1] - 1\n",
    "\n",
    "    if (fts_n == 1):\n",
    "        return df.columns[0]\n",
    "\n",
    "    infoGainMax = 0\n",
    "    for i in range(0, fts_n):\n",
    "\n",
    "        i_value_sets = df.iloc[:,i].unique()\n",
    "        Es = []\n",
    "        for value in i_value_sets:\n",
    "            mask = df.iloc[:, i] == value\n",
    "            df1 = df[mask]\n",
    "            Es.append(calEnt(df1))\n",
    "        Es_sum = sum(Es)\n",
    "\n",
    "        infoGain = E0 - Es_sum\n",
    "        \n",
    "        if (infoGain > infoGainMax):\n",
    "            infoGainMax = infoGain\n",
    "            bestFeature = df.columns[i]\n",
    "    \n",
    "    return bestFeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'no'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fish'].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.iloc[:,-1].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def creatTree(df):\n",
    "    myTree = {}\n",
    "\n",
    "    col_num = df.shape[1]\n",
    "\n",
    "    labelSet = df.iloc[:,-1].unique()\n",
    "\n",
    "    if (len(labelSet) == 1):\n",
    "        return labelSet[0]\n",
    "    if (col_num == 1):\n",
    "\n",
    "        return df.value_counts().index[0]\n",
    "\n",
    "\n",
    "\n",
    "    bestFeature = bestSplit(df)\n",
    "\n",
    "    myTree[bestFeature] = {}\n",
    "\n",
    "\n",
    "    i_value_sets = df[bestFeature].unique()\n",
    "    for value in i_value_sets:\n",
    "        mask = df[bestFeature] == value\n",
    "        df1 = df[mask].drop(columns=bestFeature)\n",
    "        myTree[bestFeature][value] = creatTree(df1)\n",
    "    \n",
    "    return myTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'no surfacing': {1: {'flippers': {1: 'yes', 0: 'no'}}, 0: 'no'}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = creatTree(df)\n",
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(inX, Tree):\n",
    "    dictInX = dict(zip(df.columns[:-1], inX))\n",
    "\n",
    "    return getChildTree(dictInX, Tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getChildTree(dictInX, Tree):\n",
    "\n",
    "    bestFeature = next(iter(Tree))\n",
    "    child = Tree[bestFeature][dictInX[bestFeature]]\n",
    "\n",
    "    if (type(child) != dict):\n",
    "        return child\n",
    "    return getChildTree(dictInX, child)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'no surfacing': {1: {'flippers': {1: 'yes', 0: 'no'}}, 0: 'no'}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creatTree(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [1, 1],\n",
       "       [1, 0],\n",
       "       [0, 1],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df.iloc[:,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\")\n",
    "X = np.array(df.iloc[:, :-1])\n",
    "y = np.array(df.iloc[:, -1])\n",
    "clf = clf.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['no surfacing', 'flippers', 'fish'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4, 0.8333333333333334, 'no surfacing <= 0.5\\nentropy = 0.971\\nsamples = 5\\nvalue = [3, 2]'),\n",
       " Text(0.2, 0.5, 'entropy = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.6, 0.5, 'flippers <= 0.5\\nentropy = 0.918\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.4, 0.16666666666666666, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.8, 0.16666666666666666, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2]')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf, feature_names=df.columns[:-1], filled=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6 (main, Nov 24 2022, 14:20:32) [GCC 9.4.0]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "d44d76ef8cbbc4331cecfe2e59228ac31ebb71026289858a116838be7168b60b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
